Semiconductor process prediction method and semiconductor process prediction apparatus considering overall features and local features

Abstract

A semiconductor process prediction method and a semiconductor process prediction apparatus considering overall features and local features are provided. The semiconductor manufacturing process prediction method includes the following steps. Several equipment sensing curves are obtained. The equipment sensing curves are filtered to reduce the co-linearity of the equipment sensing curves. A Dynamic Time Warping (DTW) procedure is performed to align the equipment sensing curves. The equipment sensing curves which are aligned are inputted into a Convolutional Neural Network (CNN) model to obtain a first prediction result considering the local features. A statistical analysis procedure is performed on the equipment sensing curves to obtain several statistical data. The statistical data are inputted into an Artificial Neural Network (ANN) model to obtain a second prediction result considering the overall features. According to the first prediction result and the second prediction result, a total prediction result is obtained.

What is claimed is: 
     
         1 . A semiconductor process prediction method considering overall features and local features, comprising:
 obtaining a plurality of equipment sensing curves;   filtering the equipment sensing curves to reduce a co-linearity of the equipment sensing curves;   performing a dynamic time warping (DTW) to align the equipment sensing curves;   inputting the equipment sensing curves which are aligned into a Convolutional Neural Network (CNN) model, to obtain a first prediction result considering the local features;   performing a statistical analysis procedure on the equipment sensing curves to obtain a plurality of statistical data;   inputting the statistical data into an Artificial Neural Network (ANN) model, to obtain a second prediction result considering the overall features; and   obtaining a total prediction result according to the first prediction result and the second prediction result.   
     
     
         2 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein each of the equipment sensing curves is composed of sensing data continuously captured over time. 
     
     
         3 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein the step of filtering the equipment sensing curves comprises:
 classifying the equipment sensing curves into a plurality of groups according to a correlation matrix; and   selecting one from the equipment sensing curves in each of the groups.   
     
     
         4 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein the CNN model is a single-channel model. 
     
     
         5 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein the CNN model is a multi-channel model. 
     
     
         6 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein in the step of obtaining the total prediction result according to the first prediction result and the second prediction result, the total prediction result is obtained through a voting procedure. 
     
     
         7 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein the CNN model is a LeNet model, an AlexNet model, a VGG model, a GoogLeNet model or a ResNet model. 
     
     
         8 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein each of the statistical data is mean, standard deviation or median. 
     
     
         9 . The semiconductor process prediction method considering the overall features and the local features according to  claim 1 , wherein the ANN model is a Supervised Learning Network, a Unsupervised Learning Network, a Hybrid Learning Network, an Associate Learning Network or an Optimization Application Network. 
     
     
         10 . A semiconductor process prediction apparatus considering overall features and local features, comprising:
 a database, configured to storing a plurality of equipment sensing curves;   a filtering unit, configured to filter the equipment sensing curves to reduce a co-linearity of the equipment sensing curves;   an aligning unit, configured to perform a dynamic time warping (DTW) to align the equipment sensing curves;   a Convolutional Neural Network (CNN) model, configured to receive the equipment sensing curves which are aligned to obtain a first prediction result considering the local features;   a statistical unit, configured to perform a statistical analysis procedure on the equipment sensing curves to obtain a plurality of statistical data;   an Artificial Neural Network (ANN) model, configured to receive the statistical data to obtain a second prediction result considering the overall features; and   a total prediction unit, configured to obtain a total prediction result according to the first prediction result and the second prediction result.   
     
     
         11 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein each of the equipment sensing curves is composed of sensing data continuously captured over time. 
     
     
         12 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the filtering unit classifies the equipment sensing curves into a plurality of groups according to a correlation matrix, and selects one from the equipment sensing curves in each of the groups. 
     
     
         13 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the CNN model is a single-channel model. 
     
     
         14 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the CNN model is a multi-channel model. 
     
     
         15 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the total prediction unit obtains the total prediction result through a voting procedure. 
     
     
         16 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the CNN model is a LeNet model, an AlexNet model, a VGG model, a GoogLeNet model or a ResNet model. 
     
     
         17 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein each of the statistical data is mean, standard deviation or median. 
     
     
         18 . The semiconductor process prediction apparatus considering the overall features and the local features according to  claim 10 , wherein the ANN model is a Supervised Learning Network, a Unsupervised Learning Network, a Hybrid Learning Network, an Associate Learning Network or an Optimization Application Network.